Small-scale Unmanned Helicopter Research Articles

L1 Adaptive Control for Small-Scale Unmanned Helicopters: Enhancing Speed Regulation

This paper focuses on the application of L1 adaptive control to the speed autopilot loop of small-scale unmanned helicopters. The L1 adaptive control technique is investigated for its potential to provide reliable speed control, particularly in handling external disturbances and model uncertainties. Although L1 adaptive control has been widely studied, its application to small-scale unmanned helicopters remains relatively unexplored. Through numerical simulations and a preliminary experimental test campaign conducted on a small rotary-wing platform, this paper contributes to validating L1 adaptive control as a promising solution for speed regulation in unmanned helicopter platforms.

Flight Control System Design and Autonomous Flight Control of Small-Scale Unmanned Helicopter Based on Nanosensors

Due to the limitation of the size and power, micro unmanned aerial vehicle (MUAV) usually has a small load capacity. Aiming at the problems of limited installation space and easy being interfered in flight attitude measurement of the small-scale unmanned helicopter (SUH), a low-cost and lightweight flight control system of the SUH based on ARM Cortex-M4 core microcontroller and Micro-Electro-Mechanical Systems (MEMS) sensors is developed in this paper. On this basis, in order to realize the autonomous flight control of SUH, firstly, the mathematical model of the SUH is given by using the Newton-Euler formulation. Secondly, a cascade flight controller consisting of the attitude controller and the position controller is developed based on linear active disturbance rejection control (LADRC) and proportional-integral-derivative (PID) control. Furthermore, simulations are conducted to validate the performance of the attitude controller and the position controller in MATLAB/SIMULINK simulation environment. Finally, based on the Align T-REX 470L SUH experimental platform, the hovering experiment and the route flight experiment are also carried out to validate the performance of the designed flight control system hardware and the proposed control algorithm. The results show that the flight control system designed in this paper has high reliability and strong anti-interference ability, and the control algorithm can effectively and reliably realize the attitude stabilization control and route control of the SUH, with high control accuracy and small error.

Adaptive Learning-Based Observer With Dynamic Inversion for the Autonomous Flight of an Unmanned Helicopter

This article presents the development of an autonomous flight control system for a small-scale unmanned helicopter based on an online adaptive learning-based observer and model predictive control (MPC). The adaptive learning-based observer provides an approximate dynamic inversion for solving the coupled dynamic problem. The discrete-time MPC (DMPC) uses a prediction process to obtain a stable model. The Laguerre function, a state observer, and a recursive learning algorithm were integrated into DMPC, the core of the flight control system. Exponential data weighting was adopted to increase the stability of the designed flight control system. By integrating these approaches, an adaptive learning-based flight control system, which consists of the pitch, roll, yaw, and heave controllers, is presented to evaluate the stability and performance of the proposed autonomous flight control system of an unmanned helicopter. Moreover, an adaptive controller based on neural approximation for yaw motion is used to improve the performance of yawing control of the helicopter. In order to validate the feasibility of the proposed method, a hardware-in-the-loop simulation was performed in real time. The system included an embedded system, a self-developed users interface, and the X-plane flight simulator. The simulation results showed that the flight control system was able to maintain the position and the attitude of the helicopter in hover mode under the influence of wind gust and disturbance. The proposed method had better performance than a robust control system, in both ideal and disturbed environments.

Attitude Tracking Control of Small-Scale Unmanned Helicopters Using Quaternion-Based Adaptive Dynamic Surface Control

In this paper, a quaternion-based adaptive dynamic surface control method is proposed for attitude tracking control for small-scale unmanned helicopters with external disturbance and uncertain dynamics. The quaternion formalism is introduced and a quaternion-based multi-input-multi-output nonlinear model is derived from the attitude dynamics of a small-scale helicopter. The low-complexity controllers are designed by the dynamic surface control method as it eliminates the problem of the explosion of items. The singularity problem is avoided by substituting Euler kinematic equations in the nonlinear model with quaternion expressions and integrating the quaternion expressions into the design process of the dynamic surface control. For improving the robustness of the control system, the radial basis function networks are applied to approximate the uncertain dynamics. The external disturbance is also compensated in the controllers’ design. This paper proves that the proposed method can guarantee the uniformly ultimate boundness of this attitude system. Simulation results are presented finally and show the effectiveness of this control approach.

Fixed Time Disturbance Observer Based Sliding Mode Control for a Miniature Unmanned Helicopter Hover Operations in Presence of External Disturbances

This paper presents a novel fixed time sliding mode disturbance observer (FTSMDO) based second-order fixed time sliding mode control (FTSMC) for small scale unmanned helicopter to do hover operations in the presence of external disturbances. Sliding mode control is insensitive to matched uncertainties but sensitive to mismatched uncertainties. The novel FTSMDO exactly estimates the total mismatched uncertainties effecting the sliding mode control performance. To counter the mismatched uncertainties a new sliding surface augmented by the total mismatched disturbance approximated by the FTSMDO is defined. The second-order FTSMC forces the system states to reach the sliding surface in fixed time and then on the sliding surface the system states exponentially converge to the desired equilibrium point. The control performance is compared with the disturbance observer based sliding mode controller (DOB-SMC) and shows superior performance.

Modeling and dynamical analysis of a small-scale unmanned helicopter

A simple model comprising five differential equations reflecting the attitude dynamics of small-scale hovering helicopters is developed. From its force analysis, this helicopter system has the dynamic structure of a Kolmogorov model producing chaos. The stable-focus mode and chaotic mode are identified for the helicopter. The hidden chaotic attraction basin is identified, which demonstrates the multi-stability of the helicopter and highly sensitivity with initial location. Varying the configuration of the moment of inertia leads to a change in dynamics for the helicopter system. The analysis of its chaotic motion is significant for designing of the controller as well as the configuration of parameters so as to avoid instabilities that produce chaos through improper assembly or selection of materials. The Lyapunov exponent spectra and the two-parameter bifurcation in terms of moment of inertia exhibit rich dynamical modes: stable, periodic orbit, pseudo-periodic orbit, and chaotic. A perturbation feedback control method is applied to control the system subjecting to chaos situation to reach the periodic orbit or equilibrium point. This control just needs a single controller but does not need the control input computation. The proposed model and the discovery of chaos provide a benchmark for the design and research of control algorithms for similar helicopter systems.

Trajectory Tracking Control for Small-Scale Unmanned Helicopters with Mismatched Disturbances Based on a Continuous Sliding Mode Approach

A novel continuous sliding mode control (CSMC) strategy based on the finite-time disturbance observer (FTDO) is proposed for the small-scale unmanned helicopters in the presence of both matched and mismatched disturbances. First, a novel sliding surface is designed based on the estimates of the mismatched disturbances and their derivatives obtained by the FTDO. Then, a continuous sliding mode control law is developed, which does not lead to any chattering phenomenon. Furthermore, the closed-loop helicopter system is proved to be asymptotically stable. Finally, the excellent hovering and tracking performance, as well as the powerful disturbance rejection capability of the proposed novel CSMC method, is validated by the simulation results.

Identification of flight dynamics models of a small-scale unmanned helicopter in hover condition

Identification of flight dynamics models of a small-scale unmanned helicopter in hover condition

Nonlinear system identification and trajectory tracking control for a flybarless unmanned helicopter: theory and experiment

This paper proposes a new nonlinear system identification method in time domain for a small-scale flybarless unmanned helicopter based on an adaptive differential evolution algorithm. By analyzing and processing the real input and output data, a nonlinear parametric model including heave dynamic model, yaw dynamic model and longitudinal–latitudinal subsystem is established. The validity of the identified model is confirmed through comparing the output of the mathematical model with the actual flight response under the same control input. In addition, a power-based identification strategy is propounded to reduce the difficulty of data acquisition and improve the efficiency of the identification process. Moreover, the trajectory tracking controller is designed and implemented based on the identified model using the backstepping control technology. The actual autonomous flight experimental results further demonstrate the accuracy of the identified nonlinear model and the effectiveness of the proposed control method.

Finite-time control for small-scale unmanned helicopter with disturbances

This work aims to develop finite-time stability property for trajectory tracking control system of small-scale unmanned helicopter subjected to uncertainties and external disturbances. The added power integrator method is applied to construct the nominal feedback control part, such that helicopters’ closed-loop system possesses highly tracking performance due to finite-time convergence property. In view of the existence of strong couplings, approximate feedback linearization is initially conducted to simplify and decouple helicopters’ input–output dynamics. Additionally, to guarantee robustness, the composite active disturbance rejection control idea is employed. These unknown perturbations are estimated using high-order sliding mode disturbance observers and compensated for directly in every virtual control law. Finite-time convergence property of the closed-loop system with disturbances is proven through Lyapunov stability theory. Finally, several comparison simulations illustrate the effectiveness and superiority of our proposed methods.

Novel integral sliding mode control for small-scale unmanned helicopters

Integral sliding mode (ISM) control which consists of a nominal control and a sliding-mode motion control, provides a nice framework for high tracking performance and good disturbance reduction. Our work develops ISM to attenuate the adverse effect of mismatched perturbations. By properly choosing sliding-manifold surface, the elimination of disturbances on control outputs enables to be achieved. Additionally, the chattering of sliding-mode control part is attenuated based on second-order sliding mode idea. Then, the proposed novel ISM control scheme is applied to address trajectory tracking problem for helicopters under perturbations. Approximated input-output linearization is implemented, such that the obtained linearized model is suitable for applying the proposed ism control. The stability of the closed-loop system for helicopter and its convergence to zeros of tracking errors are demonstrated by Lyapunov theory analysis. Several comparison simulations illustrate the effectiveness and superiority of the proposed methods.

Dynamical Model Identification for a Small-Scale Unmanned Helicopter Using an Integrated Approach

This article presents an integrated approach for the parameter identification of a small-scale unmanned helicopter. With the flight experiment data collection and preprocessing, a hybrid identified algorithm combining the improved artificial bee colony algorithm and prediction error method is proposed to obtain the unknown dynamical parameters of the linear model. The proposed algorithm is valid to use thanks to an adaptive search equation, a novel probability-scaling method, and a chaotic operator and has a good performance in search speed and quality. Afterwards, we design a wind tunnel test to modify the main rotor time constant of the identified model. The identified accuracy and feasibility of the proposed approach are verified by making a time-domain comparison with three other algorithms. Results show that the dynamical characteristics of the helicopter can be determined accurately by the identified model. And the proposed approach is propitious to enhance the reliability and availability of the identified dynamical model.

Adaptive tracking control based on neural approximation for the yaw motion of a small-scale unmanned helicopter

This article aims to study a solution that can solve the problem of tracking control for yaw motion of an unmanned helicopter. The non-affine nonlinear equation is converted to a simplified affine model. The unknown parameters are estimated by the Levenberg–Marquardt algorithm. An autonomous flight controller is developed with the Lyapunov-based adaptive controller for a discrete-time system. For flight data collection and verification purpose, the software-in-the-loop is constructed based on Simulink and X-Plane simulator. The designed system is applied in the control of the yaw motion of an R30 V2 helicopter under ideal and turbulent environments. The performance of the proposed method is compared with the fuzzy logic controller, and the simulation results show that the quality of the current approach is considerably better.

Robust Control based on ADRC and DOBC for Small-Scale Helicopter

Abstract This paper introduces and compares two robust control techniques estimating disturbances for small-scale unmanned helicopters: adaptive disturbance rejection control (ADRC) and disturbance observer based control (DOBC). The six-degree-of-freedom dynamics model of the unmanned helicopter includes modeling errors and uncertainties that degrade control performance. Furthermore, the external disturbance and complex internal coupling seriously affect the control stability of the small unmanned aerial vehicle (UAV), but this is difficult to be considered in advance for controller design. For this reason, ADRC and DOBC are designed using a minimum nominal model to compensate for internal uncertainties and external disturbances. Simulation results based on slalom maneuvers show that the proposed controller successfully compensates for disturbance and provides excellent performance compared to the case of using only the PID controller. Other than to unmanned helicopters, it is applicable to most dynamic systems that can be approximated as a double integrator system.

Robust control of unmanned helicopters with high-order mismatched disturbances via disturbance-compensation-gain construction approach

In this paper, a novel robust control strategy based on disturbance-compensation-gain (DCG) construction approach is proposed for small-scale unmanned helicopters in the presence of high-order mismatched disturbances. The overall control structure consists of two hierarchical layers. The inner-loop controller is to guarantee the stability of the unmanned helicopters subject to high-order mismatched disturbances. With the estimation of the disturbances and their successive derivatives via finite-time disturbance observer (FTDO), by properly designing some disturbance compensation gains, a novel robust controller is developed to remove the high-order mismatched disturbances from the output channels. The outer-loop controller is to produce flight commands for inner-loop system, as well as to track the reference trajectory, which is carried out with the dynamic inversion technique. The simulation results demonstrate that the unmanned helicopters are capable to perform flight missions autonomously with the proposed control strategy.

Dynamic Decoupling Control Optimization for a Small-Scale Unmanned Helicopter

This article presents design and optimization results from an implementation of a novel disturbance decoupling control strategy for a small-scale unmanned helicopter. Such a strategy is based on the active disturbance rejection control (ADRC) method. It offers an appealing alternative to existing control approaches for helicopters by combining decoupling and disturbance rejection without a detailed plant dynamics. The tuning of the control system is formulated as a function optimization problem to capture various design considerations. In comparison with several different iterative search algorithms, an artificial bee colony (ABC) algorithm is selected to obtain the optimal control parameters. For a fair comparison of control performance, a well-designed LQG controller is also optimized by the proposed method. Comparison results from an attitude tracking simulation against wind disturbance show the significant advantages of the proposed optimization control for this control application.

Artificial bee colony optimised controller for small-scale unmanned helicopter

ABSTRACTIn this paper, an efficient approach to design and optimize a flight controller of a small-scale unmanned helicopter is proposed. Given the identified helicopter model, the Linear Quadratic Gaussian/Loop Transfer Recovery (LQG/LTR) robust control method is applied for trajectory tracking and attitude control of the helicopter with a two-loop hierarchical control architecture. Since the performance of the controller extremely depends on its weighting matrices, the Artificial Bee Colony (ABC) algorithm is introduced to automatically select the parameters of the matrices. Comparative studies between optimal algorithms are also carried out. A series of flight experiments and simulations are conducted to investigate the effectiveness and robustness of the proposed optimised controller.

Robust Trajectory Tracking Control for Small-Scale Unmanned Helicopters With Model Uncertainties

This paper addresses the trajectory tracking control problem for small-scale unmanned helicopters with model uncertainties. In particular, we propose a robust control scheme that is decomposed into a position and an attitude control modules, operating in a cascaded form, and does not depend on the explicit knowledge of the value of the model parameters. Exploiting the concepts and techniques of prescribed performance control, each module guarantees trajectory tracking with prescribed transient and steady-state response, despite the presence of external disturbances, such as wind gusts and ground effect phenomena. Additionally, a novel reformulation of the translational dynamics reveals a sufficient condition for closed-loop stability and allows us to extract the appropriate attitude commands straightforwardly. Moreover, the derived control algorithms are of low complexity and thus can be efficiently implemented on low-cost, aerial embedded systems of limited power and computational resources. Finally, the efficacy of the proposed control scheme is verified via extensive experimental studies using an autonomous small-scale helicopter.

Robust adaptive tracking control for unmanned helicopter with constraints

In this article, a prescribed performance-based adaptive neural network control scheme is proposed for an uncertain small-scale unmanned helicopter system subject to input saturations and output constraints. The radial basis function neural networks are employed to approximate system uncertainties. A nonlinear disturbance observer is developed to tackle input saturation. Meanwhile, the prescribed performance function is adopted to deal with output constraint. The closed-loop system stability is rigorously proved using Lyapunov synthesis. Finally, simulation results for unmanned helicopter system are presented to demonstrate the effectiveness of developed tracking control scheme using disturbance observer and radial basis function neural network.

A real-time H∞ cubature Kalman filter based on SVD and its application to a small unmanned helicopter

In this work, the main objective is to study a real-time H∞ cubature Kalman filter (CKF). Robust CKF combines robust principle and CKF, many advantages in state estimation for a small unmanned helicopter are presented, employing singular value decomposing. Meanwhile, a real-time principle is introduced to overcome the problem of costly calculating time. Unlike many other novel CKF is verified in simulation, the proposed CKF is implemented on the small scale unmanned helicopter's inertial navigation system. A serial of experimental results on a small-scale helicopter confirm the effectiveness scheme and the accuracy state estimation of the proposed filter in high-maneuverability flight.